Obstacle distribution simulation method, device and terminal based on a probability graph

ABSTRACT

Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.201811044644.5, entitled “Obstacle Distribution Simulation Method,Device and Terminal Based on a Probability Graph”, and filed on Sep. 7,2018, which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the technical field of computers, andin particular to an obstacle distribution simulation method, device andterminal based on a probability graph.

BACKGROUND

In an off-line state of a high-precision map, labeling data of obstaclesin the map may be collected. The labeling data includes a currentposition, an orientation, an ID and a type and the like of an obstacle.The type of the obstacle may be a dynamic obstacle such as a vehicle, apedestrian and a rider, or a static obstacle such as a traffic cone. Howto simulate the number and the position distribution of the obstacles soas to reconstruct the real conditions as much as possible is drawingmore and more attention from those skilled in the art.

In the existing technical solutions, a high-precision map is generallyused, and simulation is typically performed by using obstaclearrangement based on a rule Examples of the obstacle arrangement basedon a rule include: vehicle arrangement in a direction of a lane line,and random pedestrian arrangement. However, very limited scenarios canbe presented in the obstacle arrangement based on a rule. Since thehigh-precision map includes only main roads and includes no side roadsor branch roads, a simulation result of the position distribution of thesame type of obstacles and a simulation result of the numberdistribution of different types of obstacles differ from real conditionsgreatly. In addition, the obstacle arrangement based on a rule cannotpresent all the possible cases in a real scenario in an exhaustivemanner, resulting in a low coverage.

SUMMARY

An obstacle distribution simulation method, device and terminal based ona probability graph are provided according to embodiments of the presentdisclosure, to solve at least the above technical problems in theexisting technologies.

In a first aspect, an embodiment of the present disclosure provides anobstacle distribution simulation method based on probability map, whichincludes:

acquiring a plurality of point clouds of a plurality of frames, whereineach point cloud comprises a plurality of obstacles;

acquiring real labeling data of an acquisition vehicle at vehiclelabeled positions, and acquiring data of a simulation position of theacquisition vehicle based on the real labeling data and a movement ruleof the acquisition vehicle;

determining the number of obstacles to be simulated at a position to besimulated based on the data of simulation position of the acquisitionvehicle;

extracting real labeling data of the obstacles in the plurality of pointclouds, and constructing a labeling data set with the extracted reallabeling data;

dividing the labeling data set into a plurality of grids and calculatingoccurrence probabilities of the plurality of obstacles in the pluralityof grids;

selecting the determined number of obstacles to be simulated accordingto the occurrence probabilities; and

acquiring a position distribution of the selected obstacles to besimulated for the position to be simulated based on the real labelingdata of the selected obstacles to be simulated.

In combination with the first aspect, in a first implementation of thefirst aspect of the present disclosure, the calculating occurrenceprobabilities of the plurality of obstacles in the plurality of gridsincludes:

selecting one obstacle from the labeling data set, and acquiring reallabeling data of the one obstacle;

matching a center of a Gaussian template to a position of the oneobstacle in the real labeling data, and assigning weights of theGaussian template to respective grids around the center;

traversing the real labeling data of the plurality of obstacles with theGaussian template, and accumulating weights of each grid to obtain atotal weight of the grid; and

calculating the occurrence probabilities of the plurality of obstaclesin the grids based on the total weights of the grids.

In combination with the first aspect, in a second implementation of thefirst aspect of the present disclosure, the selecting the determinednumber of obstacles to be simulated according to the occurrenceprobabilities includes:

selecting the determined number of obstacles to be simulated with anunequal probability sampling method according to the occurrenceprobabilities.

In combination with the first aspect, in a third implementation of thefirst aspect of the present disclosure, the determining the number ofobstacles to be simulated at a position to be simulated based on thedata of simulation position of the acquisition vehicle includes:

searching for real labeling data of the acquisition vehicle at thevehicle labeled position identical or adjacent to the position to besimulated; and

retrieving a point cloud acquired at the vehicle labeled position, anddetermining the number of the obstacles in the retrieved point cloud asthe number of obstacles to be simulated.

In a second aspect, an obstacle distribution simulation device based ona probability graph is provided according to an embodiment of thepresent disclosure, which includes:

a point cloud acquiring module, configured to acquire a plurality ofpoint clouds of a plurality of frames, wherein each point cloudcomprises a plurality of obstacles;

an acquisition vehicle simulating module, configured to acquire reallabeling data of an acquisition vehicle at vehicle labeled positions,and acquire data of a simulation position of the acquisition vehiclebased on the real labeling data and a movement rule of the acquisitionvehicle;

a number determining module, configured to determine the number ofobstacles to be simulated at a position to be simulated based on thedata of simulation position of the acquisition vehicle;

an data set constructing module, configured to extract real labelingdata of the obstacles in the plurality of point clouds, and construct alabeling data set with the extracted real labeling datas;

a probability calculating module, configured to divide the labeling dataset into a plurality of grids and calculate occurrence probabilities ofthe plurality of obstacles in the plurality of grids;

an obstacle selecting module, configured to select the determined numberof obstacles to be simulated according to the occurrence probabilities;and

a position distribution acquiring module, configured to acquire aposition distribution of the selected obstacles to be simulated for theposition to be simulated based on the real labeling data of the selectedobstacles to be simulated.

In combination with the second aspect, in a second implementation of thesecond aspect of the present disclosure, the probability calculatingmodule includes:

a data selecting unit, configured to select one obstacle from thelabeling data set, and acquire real labeling data of the one obstacle;

a weight assigning unit, configured to match a center of a Gaussiantemplate to a position of the one obstacle in the real labeling data,and assign weights of the Gaussian template to respective grids aroundthe center;

a traversing unit, configured to traverse the real labeling data of theplurality of obstacles with the Gaussian template, and accumulateweights of each grid to obtain a total weight of the grid; and

a probability calculating unit, configured to calculate the occurrenceprobabilities of the plurality of obstacles in the grids based on thetotal weights of the grids.

In combination with the second aspect, in a third implementation of thesecond aspect of the present disclosure, the number determining moduleincludes:

a data searching unit, configured to search for real labeling data ofthe acquisition vehicle at the vehicle labeled position identical oradjacent to the position to be simulated; and

a number determining unit, configured to retrieve a point cloud acquiredat the vehicle labeled position, and determine the number of theobstacles in the retrieved point cloud as the number of obstacles to besimulated.

In a third aspect, an obstacle distribution simulation terminal based ona probability graph is provided according to an embodiment of thepresent disclosure, which includes: a processor and a memory for storinga program which supports the obstacle distribution simulation devicebased on a probability graph in executing the obstacle distributionsimulation method based on a probability graph described above in thefirst aspect, and the processor is configured to execute the programstored in the memory. The terminal can further include a communicationinterface for enabling the terminal to communicate with other devices orcommunication networks.

The functions may be implemented by using hardware or by executingcorresponding software by hardware. The hardware or software includesone or more modules corresponding to the functions described above.

In a fourth aspect, an embodiment of the present disclosure provides acomputer readable storage medium for storing computer softwareinstructions used for an obstacle distribution simulation device basedon a probability graph, the computer readable storage medium including aprogram involved in executing the obstacle distribution simulationmethod based on a probability graph described above in the first aspectby the obstacle distribution simulation device based on a probabilitygraph.

One of the above technical solutions has the following advantages oradvantageous effects: the labeling data set formed by real labeling dataof the obstacles is divided into grids, and an occurrence probability ofan obstacle in each grid is calculated; the determined number ofobstacles to be simulated are selected according to the occurrenceprobabilities of the obstacles; and position distribution of thesimulation obstacles is obtained according to the real labeling data ofthe simulation obstacles. The selected obstacles are not limited to realobstacles, thus increasing the generality and diversity of the positionsof the obstacles.

The above summary is provided only for illustration, and is not intendedto limit the present disclosure in any way. In addition to theillustrative aspects, embodiments and features described above, furtheraspects, embodiments and features of the present disclosure may bereadily understood from the following detailed description withreference to the accompanying drawings

BRIEF DESCRIPTION OF THE DRAWINGS

Unless otherwise specified, identical or similar parts or elements aredenoted by identical reference signs throughout several figures of theaccompanying drawings. The drawings are not necessarily drawn to scale.It should be understood that these drawings merely illustrate someembodiments of the present disclosure, and should not be construed aslimiting the scope of the disclosure.

FIG. 1 is a flowchart of an obstacle distribution simulation methodbased on a probability graph according to an embodiment of the presentdisclosure;

FIG. 2 is a schematic diagram of a labeling data set according to anembodiment of the present disclosure;

FIG. 3 is a schematic diagram of dividing a labeling data set into gridswith a Gaussian template according to an embodiment of the presentdisclosure;

FIG. 4 is a block diagram of an obstacle distribution simulation devicebased on a probability graph according to an embodiment of the presentdisclosure;

FIG. 5 is a block diagram showing the structure of an obstacleprobability calculation module according to an embodiment of the presentdisclosure; and

FIG. 6 is a schematic diagram of an obstacle distribution simulationterminal based on a probability graph according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

Hereinafter, only some example embodiments are described. As can beappreciated by those skilled in the art, the described embodiments maybe modified in various different ways without departing from the spiritor scope of the present disclosure. Accordingly, the drawings and thedescription should be considered as illustrative in nature instead ofbeing restrictive.

First Embodiment

In a specific embodiment, as shown in FIG. 1, an obstacle distributionsimulation method based on a probability graph is provided, whichincludes:

Step S100: acquiring a plurality of point clouds of a plurality offrames, wherein each point cloud comprises a plurality of obstacles.

As shown in FIG. 2, when an acquisition vehicle moves along a movementroute, the acquisition vehicle may obtain a point cloud 100 by scanningsurrounding obstacles with a radar. For example, the obstacle may be abuilding 101, roadblock 102 and the like. The point cloud 100 may alsobe obtained directly externally.

Sept S200: acquiring real labeling data of an acquisition vehicle atvehicle labeled positions, and acquiring data of a simulation positionof the acquisition vehicle based on the real labeling data and amovement rule of the acquisition vehicle.

The acquisition vehicle may have a movement rule of moving along a mainroad or moving along a specified side road, and various movement rule ofthe acquisition vehicle will fall within the protection scope of theembodiments of the present disclosure. A plurality of absolutecoordinates of the acquisition vehicle along the movement route mayserve as the data of the simulation position. Moreover, an interpolationoperation may be performed to two adjacent absolute coordinates of theacquisition vehicle, that is, coordinates on a line connecting the twoadjacent absolute coordinates of the acquisition vehicle serve as thedata of the simulation position. In this way, the number of the absolutecoordinates of the acquisition vehicle (i.e., positions where theacquisition vehicle may reach) is increased, such that a simulationposition of the acquisition vehicle tends to be closer to a realposition of the acquisition vehicle.

Step S300: determining the number of obstacles to be simulated at aposition to be simulated based on the data of simulation position of theacquisition vehicle.

Real labeling data of the acquisition vehicle at the vehicle labeledposition identical or adjacent to the position to be simulated issearched, a point cloud acquired at the vehicle labeled position isretrieved, and the number of the obstacles in the retrieved point cloudis determined as the number of obstacles to be simulated.

Step S400: extracting real labeling data of the obstacles in theplurality of point clouds, and constructing a labeling data set with theextracted real labeling data.

Step S500: dividing the labeling data set into a plurality of grids andcalculating occurrence probabilities of the plurality of obstacles inthe plurality of grids.

As shown in FIG. 3, the labeling data set 200 is divided into grids. Theproperties of a grid are mainly a shape and a size of the grid. The sizeof the grid is basically defined based on a related line segment and canbe defined by the number of related line segments or the length of arelated line segment. There may be one obstacle in the grid, or theremay be multiple obstacles, or even no obstacle in the grid. A Gaussiantemplate is used to assign a weight to each grid to further calculatethe occurrence probabilities of the obstacles in each grid.

Step S600: selecting the determined number of obstacles to be simulatedaccording to the occurrence probabilities.

Step S700: acquiring a position distribution of the selected obstaclesto be simulated for the position to be simulated based on the reallabeling data of the selected obstacles to be simulated.

The real labeling data of the obstacles to be simulated is acquired, anda position distribution based on the obstacles to be simulated isfurther obtained. Diversity of position simulation of obstacles isimproved, and the simulation result of the number of obstacles and thesimulation result of the position distribution are closer to realconditions.

In an embodiment, the calculating occurrence probabilities of theplurality of obstacles in the plurality of grids includes:

selecting one obstacle from the labeling data set, and acquiring reallabeling data of the one obstacle;

matching a center of a Gaussian template to a position of the oneobstacle in the real labeling data, and assigning weights of theGaussian template to respective grids around the center;

traversing the real labeling data of the plurality of obstacles with theGaussian template, and accumulating weights of each grid to obtain atotal weight of the grid; and

calculating the occurrence probabilities of the plurality of obstaclesin the grids based on the total weights of the grids.

As shown in FIG. 3, the Gaussian template 300 is a matrix, and thenumber of rows and that of columns of the matrix can be selected asrequired. The center of the matrix has the highest weight, and weightsof remaining portions are reduced with the distance from the center.Since the occurrence probability of the selected obstacle is 1, thecenter of the Gaussian template 300 is matched to the real labeling dataof the selected obstacle. The respective remaining portions of theGaussian template 300 correspond to respective grids other than thecenter, and weights are assigned to the grids. The center of theGaussian template 300 is sequentially matched to each obstacle labeledwith real labeling data. At each time of matching, the gridscorresponding to the Gaussian template 300 are assigned to a weightrespectively once. Finally, the weights are accumulated, and each gridcorresponds to a total weight thereof. The occurrence probabilities ofobstacles in each grid are calculated based on the total weight of saideach grid.

In an embodiment, the selecting the determined number of obstacles to besimulated according to the occurrence probabilities includes:

selecting the determined number of obstacles to be simulated with anunequal probability sampling method according to the occurrenceprobabilities.

It should be noted that the selecting may be performed by other methods,including but not limited to the unequal probability sampling method,which all fall within the protection scope of the present embodiment.

In an embodiment, the obtaining the determining the number of obstaclesto be simulated at a position to be simulated based on the data ofsimulation position of the acquisition vehicle includes:

searching for real labeling data of the acquisition vehicle at thevehicle labeled position identical or adjacent to the position to besimulated; and

retrieving a point cloud acquired at the vehicle labeled position, anddetermining the number of the obstacles in the retrieved point cloud asthe number of obstacles to be simulated.

Firstly, in the real labeling data set acquired by the acquisitionvehicle along the movement route, real labeling data of the acquisitionvehicle at the vehicle labeled position identical or adjacent to theposition to be simulated is searched for. If no identical data is found,real labeling data closest to the data of the simulation position of theacquisition vehicle is searched for. A point cloud 100 acquired at thevehicle labeled position is retrieved, and the number of the obstaclesin the retrieved point cloud 100 is determined as the number of theobstacles to be simulated.

Second Embodiment

In another specific embodiment, as shown in FIG. 4, an obstacledistribution simulation device based on a probability graph is provided,which includes:

a point cloud acquiring module 10, configured to acquire a plurality ofpoint clouds of a plurality of frames, wherein each point cloudcomprises a plurality of obstacles;

an acquisition vehicle simulating module 20, configured to acquire reallabeling data of an acquisition vehicle at vehicle labeled positions,and acquire data of a simulation position of the acquisition vehiclebased on the real labeling data and a movement rule of the acquisitionvehicle;

a number determining module 30, configured to determine the number ofobstacles to be simulated at a position to be simulated based on thedata of simulation position of the acquisition vehicle;

a data set constructing module 40, configured to extract real labelingdata of the obstacles in the plurality of point clouds, and construct alabeling data set with the extracted real labeling data;

a probability calculating module 50, configured to divide the labelingdata set into a plurality of grids and calculate occurrenceprobabilities of the plurality of obstacles in the plurality of grids;

an obstacle selecting module 60, configured to select the determinednumber of obstacles to be simulated according to the occurrenceprobabilities; and

a position distribution acquiring module 70, configured to acquire aposition distribution of the selected obstacles to be simulated for theposition to be simulated based on the real labeling data of the selectedobstacles to be simulated.

In an embodiment, as shown in FIG. 5, the probability calculating module50 includes:

a data selecting unit 51, configured to select one obstacle from thelabeling data set, and acquire real labeling data of the one obstacle;

a weight assigning unit 52, configured to match a center of a Gaussiantemplate to a position of the one obstacle in the real labeling data,and assign weights of the Gaussian template to respective grids aroundthe center;

a traversing unit 53, configured to traverse the real labeling data ofthe plurality of obstacles with the Gaussian template, and accumulateweights of each grid to obtain a total weight of the grid; and

a probability calculating unit 54, configured to calculate theoccurrence probabilities of the plurality of obstacles in the gridsbased on the total weights of the grids.

In an embodiment, the number determining module includes:

a data searching unit, configured to search for real labeling data ofthe acquisition vehicle at the vehicle labeled position identical oradjacent to the position to be simulated; and

a number determining unit, configured to retrieve a point cloud acquiredat the vehicle labeled position, and determine the number of theobstacles in the retrieved point cloud as the number of obstacles to besimulated.

Third Embodiment

As shown in FIG. 6, an obstacle distribution simulation terminal basedon a probability graph is provided according to an embodiment of thepresent disclosure, which includes:

a memory 400 and a processor 500, wherein a computer program that canrun on the processor 500 is stored in the memory 400; when the processor500 executes the computer program, the obstacle distribution simulationmethod based on probability map according to the above embodiment isimplemented; the number the memory 400 and the processor 500 may each beone or more; and

a communication interface 600, configured to enable the memory 400 andthe processor 500 to communicate with an external device.

The memory 400 may include a high-speed RAM memory, or may also includea non-volatile memory, such as at least one disk memory.

If the memory 400, the processor 500 and the communication interface 600are implemented independently, the memory 400, the processor 500 and thecommunication interface 600 may be connected to each other via a bus soas to realize mutual communication. The bus may be an industry standardarchitecture (ISA) bus, a peripheral component interconnect (PCI) bus,an extended industry standard architecture (EISA) bus, or the like. Thebus may be categorized into an address bus, a data bus, a control bus orthe like. For ease of illustration, only one bold line is shown in FIG.6 to represent the bus, but it does not mean that there is only one busor only one type of bus.

Optionally, in a specific implementation, if the memory 400, theprocessor 500 and the communication interface 600 are integrated on onechip, then the memory 400, the processor 500 and the communicationinterface 600 can complete mutual communication through an internalinterface.

Fourth Embodiment

An embodiment of the present disclosure provides a computer readablestorage medium having a computer program stored thereon which, whenexecuted by a processor, implements the obstacle distribution simulationmethod based on a probability graph described in any of the aboveembodiments.

In the present specification, the description referring to the terms“one embodiment”, “some embodiments”, “an example”, “a specificexample”, or “some examples” or the like means that the specificfeatures, structures, materials, or characteristics described inconnection with the embodiment or example are contained in at least oneembodiment or example of the present disclosure. Moreover, the specificfeatures, structures, materials, or characteristics described may becombined in a suitable manner in any one or more of the embodiments orexamples. In addition, various embodiments or examples described in thespecification as well as features of different embodiments or examplesmay be united and combined by those skilled in the art, as long as theydo not contradict with each other.

Furthermore, terms “first” and “second” are used for descriptivepurposes only, and are not to be construed as indicating or implyingrelative importance or implicitly indicating the number of recitedtechnical features. Thus, a feature defined with “first” and “second”may include at least one said feature, either explicitly or implicitly.In the description of some embodiments of the present disclosure, themeaning of “a plurality” is two or more than two, unless otherwiseexplicitly or specifically indicated.

Any process or method described in the flowcharts or described otherwiseherein may be construed as representing a module, segment or portionincluding codes for executing one or more executable instructions forimplementing particular logical functions or process steps. The scope ofthe preferred embodiments of the present disclosure includes additionalimplementations in which functions may be implemented in an order thatis not shown or discussed, including in a substantially concurrentmanner or in a reverse order based on the functions involved. All theseshould be understood by those skilled in the art to which theembodiments of the present disclosure belong.

The logics and/or steps represented in the flowcharts or otherwisedescribed herein for example may be considered as an ordered list ofexecutable instructions for implementing logical functions. They can bespecifically embodied in any computer readable medium for use by aninstruction execution system, apparatus or device (e.g., acomputer-based system, a system including a processor, or another systemthat can obtain instructions from the instruction execution system,apparatus or device and execute these instructions) or for use inconjunction with the instruction execution system, apparatus or device.For the purposes of the present specification, “computer readablemedium” can be any means that can contain, store, communicate, propagateor transmit programs for use by an instruction execution system,apparatus or device or for use in conjunction with the instructionexecution system, apparatus or device. More specific examples(non-exhaustive list) of computer readable storage medium at leastinclude: electrical connection parts (electronic devices) having one ormore wires, portable computer disk cartridges (magnetic devices), randomaccess memory (RAM), read only memory (ROM), erasable programmableread-only memory (EPROM or flash memory), fiber optic devices, andportable read only memory (CDROM). In addition, the computer-readablestorage medium may even be a paper or other suitable medium on which theprograms can be printed. This is because for example the paper or othermedium can be optically scanned, followed by editing, interpretation or,if necessary, other suitable ways of processing so as to obtain theprograms electronically, which are then stored in a computer memory.

It should be understood that individual portions of the presentdisclosure may be implemented in the form of hardware, software,firmware, or a combination thereof. In the above embodiments, aplurality of steps or methods may be implemented using software orfirmware stored in a memory and executed by a suitable instructionexecution system. For example, if they are implemented in hardware, asin another embodiment, any one or a combination of the followingtechniques known in the art may be used: discrete logic circuits havinglogic gate circuits for implementing logic functions on data signals,application-specific integrated circuits having suitable combined logicgate circuits, programmable gate arrays (PGA), field programmable gatearrays (FPGA), etc.

Those skilled in the art may understand that all or part of the stepscarried in the method of the foregoing embodiments may be implemented byusing a program to instruct the relevant hardware, and the program maybe stored in a computer readable storage medium. When executed, theprogram includes one or a combination of the steps in the methodembodiments.

In addition, individual functional units in various embodiments of thepresent disclosure may be integrated in one processing module, orindividual units may also exist physically and independently, or two ormore units may also be integrated in one module. The above integratedmodule can be implemented in the form of hardware or in the form of asoftware functional module. The integrated module may also be stored ina computer readable storage medium if it is implemented in the form of asoftware function module and sold or used as a stand-alone product. Thestorage medium may be a read-only memory, a magnetic disk or an opticaldisk, etc.

The above description only relates to specific embodiments of thepresent disclosure, but the scope of protection of the presentdisclosure is not limited thereto, and any of those skilled in the artcan readily contemplate various changes or replacements within thetechnical scope of the present disclosure. All these changes orreplacements should be covered by the scope of protection of the presentdisclosure. Therefore, the scope of protection of the present disclosureshould be determined by the scope of the appended claims.

What is claimed is:
 1. An obstacle distribution simulation method basedon a probability graph, comprising: acquiring a plurality of pointclouds of a plurality of frames, wherein each point cloud comprises aplurality of obstacles; acquiring real labeling data of an acquisitionvehicle at vehicle labeled positions, and acquiring data of a simulationposition of the acquisition vehicle based on the real labeling data anda movement rule of the acquisition vehicle; determining a number ofobstacles to be simulated at a position to be simulated based on thedata of simulation position of the acquisition vehicle; extracting reallabeling data of the obstacles in the plurality of point clouds, andconstructing a labeling data set with the extracted real labeling data;dividing the labeling data set into a plurality of grids and calculatingoccurrence probabilities of the plurality of obstacles in the pluralityof grids; selecting the determined number of obstacles to be simulatedaccording to the occurrence probabilities; and acquiring a positiondistribution of the selected number of obstacles to be simulated for theposition to be simulated based on the real labeling data of the selectedobstacles to be simulated.
 2. The obstacle distribution simulationmethod of claim 1, wherein the calculating occurrence probabilities ofthe plurality of obstacles in the plurality of grids comprises:selecting one obstacle from the labeling data set, and acquiring reallabeling data of the one obstacle; matching a center of a Gaussiantemplate to a position of the one obstacle in the real labeling data,and assigning weights of the Gaussian template to respective gridsaround the center; traversing the real labeling data of the plurality ofobstacles with the Gaussian template, and accumulating weights of eachgrid to obtain a total weight of the grid; and calculating theoccurrence probabilities of the plurality of obstacles in the gridsbased on the total weights of the grids.
 3. The obstacle distributionsimulation method of claim 1, wherein the selecting the determinednumber of obstacles to be simulated according to the occurrenceprobabilities comprises: selecting the determined number of obstacles tobe simulated with an unequal probability sampling method according tothe occurrence probabilities.
 4. The obstacle distribution simulationmethod of claim 1, wherein the determining the number of obstacles to besimulated at a position to be simulated based on the data of simulationposition of the acquisition vehicle comprises: searching for reallabeling data of the acquisition vehicle at the vehicle labeled positionidentical or adjacent to the position to be simulated; and retrieving apoint cloud acquired at the vehicle labeled position, and determiningthe number of the obstacles in the retrieved point cloud as the numberof obstacles to be simulated.
 5. An obstacle distribution simulationdevice based on a probability graph, comprising: one or more processors;and a storage device configured for storing one or more programs,wherein the one or more programs, when executed by the one or moreprocessors, cause the one or more processors to: acquire a plurality ofpoint clouds of a plurality of frames, wherein each point cloudcomprises a plurality of obstacles; acquire real labeling data of anacquisition vehicle at vehicle labeled positions, and acquire data of asimulation position of the acquisition vehicle based on the reallabeling data and a movement rule of the acquisition vehicle; determinea number of obstacles to be simulated at a position to be simulatedbased on the data of simulation position of the acquisition vehicle;extract real labeling data of the obstacles in the plurality of pointclouds, and construct a labeling data set with the extracted reallabeling data; divide the labeling data set into a plurality of gridsand calculate occurrence probabilities of the plurality of obstacles inthe plurality of grids; select the determined number of obstacles to besimulated according to the occurrence probabilities; and acquire aposition distribution of the selected number of obstacles to besimulated for the position to be simulated based on the real labelingdata of the selected obstacles to be simulated.
 6. The obstacledistribution simulation device of claim 5, wherein the one or moreprograms are executed by the one or more processors to enable the one ormore processors to: select one obstacle from the labeling data set, andacquire real labeling data of the one obstacle; match a center of aGaussian template to a position of the one obstacle in the real labelingdata, and assign weights of the Gaussian template to respective gridsaround the center; traverse the real labeling data of the plurality ofobstacles with the Gaussian template, and accumulate weights of eachgrid to obtain a total weight of the grid; and calculate the occurrenceprobabilities of the plurality of obstacles in the grids based on thetotal weights of the grids.
 7. The obstacle distribution simulationdevice of claim 5, wherein the one or more programs are executed by theone or more processors to enable the one or more processors to: searchfor real labeling data of the acquisition vehicle at the vehicle labeledposition identical or adjacent to the position to be simulated; andretrieve a point cloud acquired at the vehicle labeled position, anddetermine the number of the obstacles in the retrieved point cloud asthe number of obstacles to be simulated.
 8. A non-transitory computerreadable storage medium having computer programs stored thereon that,when executed by a processor, cause the processor to perform the methodof claim 1.